Trading Out of Sight: An Analysis of Cross-Trading in Mutual Fund Families,

Journal of Financial Economics (2020), with Alexander Eisele, Gianpaolo Parise, and Kim Peijnenburg  

This paper explores the incentives for mutual funds to trade with sibling funds affiliated with the same group. To this end, we construct a dataset of almost one million equity transactions and compare the pricing of trades crossed internally (cross-trades) with that of twin trades executed with external counterparties. We find that cross-trades are used either to opportunistically reallocate performance among trading funds or to reduce transaction costs for both counterparties. The prevalent incentive depends on the intensity of internal monitoring and the market state. We discuss the implications for the literature on fund performance and the current regulatory debate. 

Journal of Corporate Finance (2020), with Giuseppe Pratobevera

Conferences:  2018 JCF Special Issue Conference, The Hong Kong Polytechnic University, SFI Research Days (Gerzensee, 2018), Northern Finance Association Conference (Charlevoix, 2018)

We document a robust buy/sell asymmetry in the choice of the broker in the IPO aftermarket: institutions that sell IPO shares through non-lead brokers tend to have bought them through the lead underwriters in the aftermarket. This trading behavior is consistent with institutional investors hiding their sell trades and breaking their laddering agreements with the lead underwriters. The asymmetry is the strongest in cold IPOs and is limited exclusively to the first month after the issue when the incentives not to be detected are the strongest. We show that the intention to flip IPO allocations is not an important motive for hiding sell trades from the lead underwriters. We find that hiding sell trades is an effective strategy to circumvent underwriters' monitoring mechanisms: the more institutions hide their sell trades, the less they are penalized in subsequent IPO allocations.


Is There Skill in the Game? Institutional IPO Allocations (new version coming soon!), with David Brown and Sergei Kovbasyuk

Presentations: Virginia Tech (2019)

The lack of reliable data about IPO allocations to investors and commissions investors pay to underwriters, resulted in very few attempts to identify empirically which investors benefit the most from participating in IPOs or to pinpoint the exact channel which enables certain investors to buy IPOs with the highest first-day returns. How much of the money left on the table goes to investors and what are they compensated for? What factors lead to differential IPO allocations? Instead of focusing on one channel, we analyze the relative importance of each (commissions paid by the investors, holding period, flipping activity, industry specialization). By focusing on investors' characteristics we learn more about IPO allocations and distinguish between information and quid-pro-quo explanations. Our results show that allocations are weakly and sometimes insignificantly related to commissions and are significantly related to investor size and specialization. This suggests that business relationships among investors and underwriters do not primarily drive allocations, while information and value-add are more important. 

Conferences/Presentations: FMA Conference (Boston, 2017), NYU Stern School of Business (2014), Northern Finance Association Conference (Ottawa, 2014), Midwest-Finance Association Conference (Orlando, 2014), EFMA "MERTON H. MILLER" doctoral seminar (Braga, 2011) 

Using transaction-level data, I analyze information leakage of financial analyst recommendations to their elite clients, as well as characteristics of the institutional investors receiving such advance knowledge. I find that investment managers who have an established relationship with their brokers trade in the direction of the research in the 5-day period before the analyst coverage initiations. My results suggest that clients enjoying a privileged relationship with their broker receive and use the pre-released information in their trades.

Conferences/Presentations: 12th Financial Risks International Forum (Paris), 2nd QFFE International Conference (Marseille), International Conference on Fintech & Financial Data Science (University College Dublin (UCD), Ireland)

Using a large database of the US institutional investors' trades, this paper sheds new light on the question of anomalies-based portfolio transaction costs. We find that the real costs paid by large investors to implement the well-identified Fama-French anomalies (size, value, investment, and profitability) and Carhart momentum are significantly lower than documented in the previous studies. We show that the average investor pays an annual transaction cost of 16bps for size, 23bps for value, 31bps for investment and profitability and 222bps for momentum. The five strategies generate statistically significant net returns after accounting for transaction costs of respectively 4.29%, 1.98%, 4.45%, 2.69%, and 2.86%. When the market impact is taken into account, transaction costs reduce substantially the profitability of the well-known anomalies for large portfolios, however, these anomalies remain profitable for average size portfolios. The break-even capacities in terms of fund size are $ 184 billion for size, $ 38 billion for value, $ 17 billion for profitability, $ 14 billion for investment and $ 410 million for momentum.

Are Star Funds Really Shining? (2016), 

with Alexander Eisele and Gianpaolo Parise, BIS Working Paper n°577

Media coverage: Bloomberg, Reuters,
Major conferences: FIRS (2016), the 9th Paris Annual Hedge Fund and Private Equity Research (2016)

The majority of financial trades take place in open and highly regulated markets. As an alternative venue, large asset managers sometimes offset the trades of affiliated funds in an internal market, without relying on external facilities or supervision. In this paper, we employ institutional trade-level data to examine such cross-trades. We find that cross-trades used to display a spread of 46 basis points with respect to open market trades before more restrictive regulation was adopted. The introduction of tighter supervision decreased this spread by 59 basis points, bringing the execution price of cross-trades below that of open market trades. We additionally find that cross-trades presented larger deviations from benchmark prices when the exchanged stocks were illiquid and highly volatile, during high financial uncertainty times, and when the asset manager had weak governance, large internal markets, and a strong incentive for reallocating performance. Finally, we provide evidence suggesting that cross-trades are more likely than open-market trades to be executed exactly at the highest or lowest price of the day, consistent with the ex-post setting of the price. Our results are consistent with theoretical models of internal capital markets in which the headquarters actively favors its "stars" at the expense of the least valuable units.

Predation versus Cooperation in Mutual Fund Families (2014), 

with Alexander Eisele and Gianpaolo Parise

Major conferences: AFA (2014), EFA (2015)

In this paper, we investigate how mutual funds react to the distress of another fund in the same fund family. We test three alternative hypotheses: (1) funds help the distressed fund, (2) funds front-run the distressed fund improving their relative performance in the fund family and, (3) the family coordinates and benefits from frontrunning the distressed fund. Our results suggest that fund managers front-run their distressed siblings and that this is the outcome of a coordinated strategy. First, we find that funds in the same family exhibit higher risk-adjusted returns when one of the funds in the family is in distress. Second, distressed funds have lower returns for a given outflow when they have a high portfolio overlap with their siblings. Third, consistent with a coordinated strategy on the family level we find that the higher risk-adjusted returns are clustered among the most important funds of the family.


Front-trading and Information Environment in Mutual Fund Families, with Richard Evans and Gianpaolo Parise

Security Lending and Fund Fees, with Gianpaolo Parise and Marius Zoican

The Impact of CoCo bonds and Write Down Bonds on Banks' Risk Appetite and Investment Policywith Cecilia Aquila, Giuseppe Pratobevera, and Alessio Ruzza

  • Winner of the  Europlace Institute of Finance (EIF) and the Labex Louis Bachelier grant

Institutional ownership and analysts’ forecasting behavior

Presented at the 9th Swiss Doctoral Workshop in Finance (Gerzensee, 2010), Global Finance Conference in (Poznan, 2010), FMA European Conference Doctoral Consortium (Hamburg, 2010)
This paper brings together three parties: financial analysts, firms and their investors in order to shed additional light on analysts’ forecasting behavior. I use data on companies’ institutional holdings and find that analysts provide more accurate annual earnings forecasts for firms with higher institutional investor ownership. Furthermore, I find evidence suggestive of analysts’ strategic “up-down” bias when giving their forecasts for firms with high institutional ownership. I conclude that they tactically issue positively biased forecasts at the beginning of a period and give subsequent pessimistic forecasts. Analysts who apply this strategy give more informative forecasts than their peers. I also discover that analysts working for larger brokers and analysts with higher firm-specific experience are more likely to act strategically.
Remote forecasts and stock returns with Marina Druz

This study investigates the revelation of private information from analysts to the market. Consistent with previous studies we document that remoteness of earnings forecast from prior consensus (average of other existing forecasts) is positively correlated with accuracy. Therefore, we conclude that remote forecasts provide new information to investors. We document that purchasing (selling short) stocks in response to the forecasts significantly higher (lower) than the existing consensus, yields abnormal gross returns. The result holds after taking transaction costs into consideration. The returns are higher for the forecasts issued by analysts working for mid-sized brokers. Analysts from small brokers apparently remain unnoticed or have a higher likelihood to err; analysts from big brokers impact the market too fast.